iMAP: an integrated bioinformatics and visualization pipeline for microbiome data analysis.
16S rRNA gene
Bioinformatics pipeline
Microbial community
Microbiome bioinformatics
Microbiome data analysis
Microbiome data visualization
Phylogenetic analysis
Phylogenetic annotation
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
03 Jul 2019
03 Jul 2019
Historique:
received:
29
01
2019
accepted:
24
06
2019
entrez:
5
7
2019
pubmed:
5
7
2019
medline:
11
9
2019
Statut:
epublish
Résumé
One of the major challenges facing investigators in the microbiome field is turning large numbers of reads generated by next-generation sequencing (NGS) platforms into biological knowledge. Effective analytical workflows that guarantee reproducibility, repeatability, and result provenance are essential requirements of modern microbiome research. For nearly a decade, several state-of-the-art bioinformatics tools have been developed for understanding microbial communities living in a given sample. However, most of these tools are built with many functions that require an in-depth understanding of their implementation and the choice of additional tools for visualizing the final output. Furthermore, microbiome analysis can be time-consuming and may even require more advanced programming skills which some investigators may be lacking. We have developed a wrapper named iMAP (Integrated Microbiome Analysis Pipeline) to provide the microbiome research community with a user-friendly and portable tool that integrates bioinformatics analysis and data visualization. The iMAP tool wraps functionalities for metadata profiling, quality control of reads, sequence processing and classification, and diversity analysis of operational taxonomic units. This pipeline is also capable of generating web-based progress reports for enhancing an approach referred to as review-as-you-go (RAYG). For the most part, the profiling of microbial community is done using functionalities implemented in Mothur or QIIME2 platform. Also, it uses different R packages for graphics and R-markdown for generating progress reports. We have used a case study to demonstrate the application of the iMAP pipeline. The iMAP pipeline integrates several functionalities for better identification of microbial communities present in a given sample. The pipeline performs in-depth quality control that guarantees high-quality results and accurate conclusions. The vibrant visuals produced by the pipeline facilitate a better understanding of the complex and multidimensional microbiome data. The integrated RAYG approach enables the generation of web-based reports, which provides the investigators with the intermediate output that can be reviewed progressively. The intensively analyzed case study set a model for microbiome data analysis.
Sections du résumé
BACKGROUND
BACKGROUND
One of the major challenges facing investigators in the microbiome field is turning large numbers of reads generated by next-generation sequencing (NGS) platforms into biological knowledge. Effective analytical workflows that guarantee reproducibility, repeatability, and result provenance are essential requirements of modern microbiome research. For nearly a decade, several state-of-the-art bioinformatics tools have been developed for understanding microbial communities living in a given sample. However, most of these tools are built with many functions that require an in-depth understanding of their implementation and the choice of additional tools for visualizing the final output. Furthermore, microbiome analysis can be time-consuming and may even require more advanced programming skills which some investigators may be lacking.
RESULTS
RESULTS
We have developed a wrapper named iMAP (Integrated Microbiome Analysis Pipeline) to provide the microbiome research community with a user-friendly and portable tool that integrates bioinformatics analysis and data visualization. The iMAP tool wraps functionalities for metadata profiling, quality control of reads, sequence processing and classification, and diversity analysis of operational taxonomic units. This pipeline is also capable of generating web-based progress reports for enhancing an approach referred to as review-as-you-go (RAYG). For the most part, the profiling of microbial community is done using functionalities implemented in Mothur or QIIME2 platform. Also, it uses different R packages for graphics and R-markdown for generating progress reports. We have used a case study to demonstrate the application of the iMAP pipeline.
CONCLUSIONS
CONCLUSIONS
The iMAP pipeline integrates several functionalities for better identification of microbial communities present in a given sample. The pipeline performs in-depth quality control that guarantees high-quality results and accurate conclusions. The vibrant visuals produced by the pipeline facilitate a better understanding of the complex and multidimensional microbiome data. The integrated RAYG approach enables the generation of web-based reports, which provides the investigators with the intermediate output that can be reviewed progressively. The intensively analyzed case study set a model for microbiome data analysis.
Identifiants
pubmed: 31269897
doi: 10.1186/s12859-019-2965-4
pii: 10.1186/s12859-019-2965-4
pmc: PMC6610863
doi:
Substances chimiques
RNA, Ribosomal, 16S
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
374Subventions
Organisme : Defense Threat Reduction Agency
ID : HDTRA1-16-1-0005
Références
PLoS Comput Biol. 2009 Apr;5(4):e1000352
pubmed: 19360128
Gut Microbes. 2012 Jul-Aug;3(4):383-93
pubmed: 22688727
Genome Res. 2011 Mar;21(3):494-504
pubmed: 21212162
Biotechniques. 2006 Apr;40(4):499-507
pubmed: 16629397
PeerJ. 2015 Dec 08;3:e1487
pubmed: 26664811
Bioinformatics. 2016 Oct 1;32(19):3047-8
pubmed: 27312411
PLoS One. 2016 Oct 5;11(10):e0163962
pubmed: 27706213
PeerJ. 2016 Oct 18;4:e2584
pubmed: 27781170
Nucleic Acids Res. 2005 Jan 1;33(Database issue):D294-6
pubmed: 15608200
Genome Biol. 2011 Jun 24;12(6):R60
pubmed: 21702898
mSphere. 2017 Mar 8;2(2):
pubmed: 28289728
Appl Environ Microbiol. 2013 Sep;79(17):5112-20
pubmed: 23793624
Int J Syst Evol Microbiol. 2017 May;67(5):1613-1617
pubmed: 28005526
Nucleic Acids Res. 2016 Jul 8;44(W1):W242-5
pubmed: 27095192
F1000Res. 2016 Jun 24;5:1492
pubmed: 27508062
Nat Methods. 2016 Jul;13(7):581-3
pubmed: 27214047
Nucleic Acids Res. 2014 Jan;42(Database issue):D643-8
pubmed: 24293649
Mol Biol Evol. 2013 Apr;30(4):772-80
pubmed: 23329690
Appl Environ Microbiol. 2009 Dec;75(23):7537-41
pubmed: 19801464
BMC Bioinformatics. 2014 Aug 29;15:293
pubmed: 25176396
Appl Environ Microbiol. 2006 Jul;72(7):5069-72
pubmed: 16820507
Bioinformatics. 2012 Oct 1;28(19):2520-2
pubmed: 22908215
ISME J. 2011 Feb;5(2):169-72
pubmed: 20827291
Microbiome. 2018 May 17;6(1):90
pubmed: 29773078
Curr Protoc Microbiol. 2012 Nov;Chapter 1:Unit 1E.5.
pubmed: 23184592
J Open Res Softw. 2018;3(30):
pubmed: 31552137
Bioinformatics. 2011 Aug 15;27(16):2194-200
pubmed: 21700674
Bioinformatics. 2018 Jul 15;34(14):2371-2375
pubmed: 29506021